Communicating with the comptuer

• R (~English)
  • Programming language, allows you to communicate with the computer
• RStudio Integrated Development Environment (~Word)
  • Makes using R as effective and efficient as possible:
    • Code editor
    • Nice extras: syntax highlighting; code completion; file explorer; help

install.packages("ggplot2") # Install new package (you only need to do it once)
library(ggplot2) # Load the package

RStudio

RStudio

• Source editor
  • Write your R code (load data, clean it, model it, etc)
• Environment/Workplace
  • All the variables that you have defined
• Files
  • File explorer, find your files.
• Help
  • Get information about code (super useful!)
• Console
  • Write R code (not recommended at this point) and see the output of your R scripts
• Plots
  • See the plots, and export it

• History
  • History of all the code you have run.
• Packages
  • All packages that you have loaded (I don’t recommend loading/unloading packages this way)
• Terminal
  • Run commands on your terminal (this is not R, you won’t need to use this)

Basic units of RStudio

Run pieces of code

View your data/objects

Create a new file: R Scripts and Quarto Documents

Paths and working directory

• Your computer finds files by their path:
  • /Users/yourname/Desktop/someproject/data/somefile.csv (Mac/Linux)
  • C:\\Users\\yourname\\Desktop\\someproject\\data\\somefile.csv (Windows)
• Both in Unix and Windows:
  • Always use / (R will convert it)
  • \\ is a “escape character”, with a special meaning

Absolute vs relative paths

• Absolute (e.g., the ones just shown)
  • The “full” path
  • Defined from the root directory, the top-most directory on your machine
• Relative (e.g. data/somefile.csv)
  • Only the “relevant” piece of the path
  • Defined from the working directory
  • Makes it possible to use the code in different computers
  • Interpreted as: in the current working directory, find the folder “data”, and inside, the file “somefile.csv”

Working directory

Changing the working directory with RStudio:

• “Session (top menu) -> Set Working directories”
• “Files (RStudio unit) -> More –> Set as working directory”
• On a notebook –> Working directory = directory of the notebook

. . .

Important shortcuts for relative paths:

• “../somefile.csv”: find “somefile.csv” one level down
• “../../somefile.csv”: find “somefile.csv” two levels down
• “./somefile.csv”: find “somefile.csv” in the current level (not so useful, it is identical to “somefile.csv”)
• “~/somefile.csv”: find “somefile.csv” in your home directory

R-scripts (.R)

• Text file with R code (you can open it in any text editor)
• Working directory:
  • If not associated to a project: your home directory (e.g. /Users/yourname)
  • If associated to a project: the project directory
• Output in the console/plots menus

. . .

Rstudio-projects (.Rproj)

• Contains a .Rproj file within the directory, with project options. More info on setting up projects
• Working directory: the project directory where the .Rproj file resides.

R-notebooks (.Rmd) and Quarto documents (.qmd)

• Markdown files (like this one!) – combines chunks of texts and code
• Working directory: the directory where the document is located
• Outputs directly in the editor. Can be knitted to HTML/PDF/Word
• Most useful for data science

. . .

Variables

• Tell the computer to save an object (a number, a string, a spreadsheet) with a name.

• Creating variables in R is very straightforward:

  • you just use the assignment operator, <-

a <- 100
print(a)

[1] 100

Code

• Instructions to modify variables
• Can be organized in functions: blocks of code that take some input and return some output

sum(a, 1)

[1] 101

a |> sum(1)

[1] 101

Objects

Everything is an object in R, and can be assigned to a variable name

Basic data types (elements)

• character: “some text”
• numeric: e.g., 2.1
• integer: e.g., 2L
• logical: TRUE/FALSE
• factor: e.g., factor(“amsterdam”)

Basic data structures

• Consist of data types and functions to transform them
  • vector: c(2, 4, 2)
  • list: list(first_col = 1, second = “a”, third = TRUE)
  • matrix: matrix(c(4, 4, 4, 4), nrow = 2, ncol = 2)
  • data.frame: The most important ~ spreadsheet

The help

• Everything that is published on the Comprehensive R Archive Network (CRAN) and is aimed at R users, must be accompanied by a help file.

• If you know the name of the function that performs an operation, e.g. anova(), then you just type ?anova or help(anova) in the console, or use the “Help” menu.

• If you do not know the name of the function: type ?? followed by your search criterion. For example ??anova returns a list of all help pages that contain the word ‘anova’

• Alternatively, the internet will tell you almost everything you’d like to know and sites such as http://www.stackoverflow.com and http://www.stackexchange.com, as well as Google and LLM can be of tremendous help.

  • If you google R related issues; use ‘R:’ as a prefix in your search term
  • On social media, the #RStats is often used to discuss R

Calling objects

• You just use type the name you have given to the object

• For example, we assigned the value 100 to object a.

a <- 100

a

[1] 100

Using functions

# This is a comment, it won't be read by R
student_number <- 4
paste("The number of students is: ", student_number, sep = " ")

[1] "The number of students is:  4"

#sep can be any character, or "\n" (newline), "\t" (tab),

vec <- c(1, 2, 3, 4)
mean(vec)

[1] 2.5

mean(1:4)

[1] 2.5

Using packages

# Install a package to read `csv` files
install.packages("readr") 

# Loading the package, usually at the top of the R script
library(readr)

# Using the readr library
data <- readr::read_csv("../common_datasets/dataset_boys.csv", col_select = c("age","hgt"))

Hint: the readr:: is optional when the package is loaded

Data analysis

# Summary statistics
summary(data)
##       age              hgt        
##  Min.   : 0.035   Min.   : 50.00  
##  1st Qu.: 1.581   1st Qu.: 84.88  
##  Median :10.505   Median :147.30  
##  Mean   : 9.159   Mean   :132.15  
##  3rd Qu.:15.267   3rd Qu.:175.22  
##  Max.   :21.177   Max.   :198.00  
##                   NA's   :20

Practical 1

Goal: Get used to RStudio using R as a calculator, and install one library

1. Create an R script
2. Create an R project
3. Create a Quarto document